APT-RL first learns reusable locomotion representations from trajectory-optimization data and then uses these representations as priors for reinforcement learning on complex terrain.
Trajectory optimization based on single rigid body dynamics generated 180,000 trajectories (15.5 hours of motion) in 8 minutes. The dataset contains both state trajectories and their corresponding control inputs.
source & further reading
skillquadsr.github.io — original article